Combating disinformation in modern conflict reporting: How international media are using Open-Source Intelligence (OSINT) in their coverage of the Russia-Ukraine war
Bibliographic record
Abstract
This thesis examines how international media are using Open-Source Intelligence (OSINT) in their coverage of the Russia-Ukraine war. A mixed-methods approach employed conceptual content analysis, and textual analysis to a sample of AP News, BBC News, and Reuters coverage published between 24 February 2022 and 31 December 2022. The results showed that only 38.2% of coverage contained some form of OSINT analysis. In instances where OSINT content was used, the common types of OSINT material analysed included maps, satellite imagery, and visual footage. The most common methods of presenting the analysis included maps, text and images. While the media collaborated with external partners on 47.8% of the analysis, about a quarter was handled in-house, with 26.5% of the analysis sourced externally. The Institute for the Study of War, Maxar Technologies and Planet Labs emerged as the top sources of OSINT analysis, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".